Date of Submission

Spring 5-10-2018

Degree Type


Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

Committee Chair/First Advisor

Dan Lo




Dan Lo

Committee Member

Dan Lo

Committee Member

Mingon Kang

Committee Member

Qian Kai


Malware classification is a critical part in the cybersecurity.

Traditional methodologies for the malware classification

typically use static analysis and dynamic analysis to identify malware.

In this paper, a malware classification methodology based

on its binary image and extracting local binary pattern (LBP)

features are proposed. First, malware images are reorganized into

3 by 3 grids which is mainly used to extract LBP feature. Second,

the LBP is implemented on the malware images to extract features

in that it is useful in pattern or texture classification. Finally,

Tensorflow, a library for machine learning, is applied to classify

malware images with the LBP feature. Performance comparison

results among different classifiers with different image descriptors

such as GIST, a spatial envelope, and the LBP demonstrate that

our proposed approach outperforms others.